Daymon - Data Engineer


Python, SQL, SQL Server

Daymon is recruiting a Data Engineer

About: Daymon drives Private Brand innovation, differentiation and results. They are the only solution provider that influences all aspects of Private Brand development, from strategy to execution to consumer engagement. Their unique approach helps retailers and brands set themselves apart — boosting brand presence, category effectiveness and speed to shelf. Daymon offers a full suite of best-in-class Private Brand development services, including: 
  • Strategy, analytics and insights
  • Product development and sourcing
  • Supplier development and management
  • Sales execution and account management
  • Design and packaging management 
About the role:
  • Experience in the retail environment and ability to understand business
  • Ability to multi-task, set priorities and work independently in a fast-paced and rapidly changing environment
  • Able to manage projects, processes, timelines, and multiple stakeholders at various levels
  • Strong interest in developing a Business Intelligence area.
  • Have a proven problem-solving mindset and skills.


  • Degree in Data Science, Mathematics, Engineering, or closely related fields (or equivalent experience); Master’s degree preferred.
  • Minimum three years of related work experience.
  • Skills in programming languages such as Python are highly appreciated.
  • Experience with SQL and relational databases and have experience with data integration tools and ETL processes, data warehousing concepts.
  • Excellent understanding and sound experience with SQL scripting (preferred Microsoft SQL Server), with experience designing and implementing data models, optimized for performance, scalability, and ease of use, and be familiar with schema design, normalization, indexing, and partitioning.
  • Experience managing backhand solutions and servers (Tableau & SQL servers preferred).
  • Proficiency in cloud solutions migration and optimizing data pipelines and data warehousing in the cloud.


  • Define strategies for system structure implementation and evolution according to business needs.
  • Data pipeline development: Developing and maintaining efficient, scalable, and reliable data pipelines that collect, transform, and load data from various sources into a central repository, such as a data warehouse.
  • Performance optimization: Optimizing data processing performance by fine-tuning ETL processes, optimizing database queries, and tuning hardware and software configurations
  • Data transformation: Transforming raw data into a structured format that is suitable for analysis and reporting
  • Management of Tableau servers, SQL servers, Virtual Machines and other software solutions.
  • Processing, cleansing, and validating the integrity of data to be used for analysis
  • Continuously evaluate new technologies and tools and provide recommendations on improving the data platform.
  • Work closely with data scientists, data analysts, and other stakeholders to understand their needs and develop solutions to meet those needs.


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